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410
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1 import argparse
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2 import utils.general_utils as utils
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3 from typing import Optional, List
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4 import os
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5 import numpy as np
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6 import pandas as pd
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7 import cobra
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8 import utils.CBS_backend as CBS_backend
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9 from joblib import Parallel, delayed, cpu_count
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10 from cobra.sampling import OptGPSampler
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11 import sys
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12
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13
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14 ################################# process args ###############################
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15 def process_args(args :List[str] = None) -> argparse.Namespace:
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16 """
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17 Processes command-line arguments.
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18
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19 Args:
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20 args (list): List of command-line arguments.
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21
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22 Returns:
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23 Namespace: An object containing parsed arguments.
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24 """
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25 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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26 description = 'process some value\'s')
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27
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28 parser.add_argument("-mo", "--model_upload", type = str,
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29 help = "path to input file with custom rules, if provided")
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30
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31 parser.add_argument('-ol', '--out_log',
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32 help = "Output log")
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33
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34 parser.add_argument('-td', '--tool_dir',
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35 type = str,
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36 required = True,
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37 help = 'your tool directory')
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38
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39 parser.add_argument('-in', '--input',
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40 required = True,
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41 type=str,
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42 help = 'inputs bounds')
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43
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44 parser.add_argument('-ni', '--names',
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45 required = True,
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46 type=str,
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47 help = 'cell names')
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48
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49 parser.add_argument('-a', '--algorithm',
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50 type = str,
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51 choices = ['OPTGP', 'CBS'],
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52 required = True,
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53 help = 'choose sampling algorithm')
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54
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55 parser.add_argument('-th', '--thinning',
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56 type = int,
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57 default= 100,
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58 required=False,
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59 help = 'choose thinning')
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60
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61 parser.add_argument('-ns', '--n_samples',
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62 type = int,
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63 required = True,
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64 help = 'choose how many samples')
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65
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66 parser.add_argument('-sd', '--seed',
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67 type = int,
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68 required = True,
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69 help = 'seed')
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70
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71 parser.add_argument('-nb', '--n_batches',
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72 type = int,
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73 required = True,
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74 help = 'choose how many batches')
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75
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76 parser.add_argument('-ot', '--output_type',
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77 type = str,
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78 required = True,
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79 help = 'output type')
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80
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81 parser.add_argument('-ota', '--output_type_analysis',
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82 type = str,
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83 required = False,
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84 help = 'output type analysis')
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85
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86 parser.add_argument('-idop', '--output_path',
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87 type = str,
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88 default='flux_simulation',
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89 help = 'output path for maps')
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90
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91 ARGS = parser.parse_args(args)
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92 return ARGS
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93
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94 ########################### warning ###########################################
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95 def warning(s :str) -> None:
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96 """
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97 Log a warning message to an output log file and print it to the console.
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98
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99 Args:
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100 s (str): The warning message to be logged and printed.
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101
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102 Returns:
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103 None
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104 """
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105 with open(ARGS.out_log, 'a') as log:
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106 log.write(s + "\n\n")
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107 print(s)
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108
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109
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110 def write_to_file(dataset: pd.DataFrame, name: str, keep_index:bool=False)->None:
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111 dataset.index.name = 'Reactions'
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112 dataset.to_csv(ARGS.output_path + "/" + name + ".csv", sep = '\t', index = keep_index)
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113
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114 ############################ dataset input ####################################
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115 def read_dataset(data :str, name :str) -> pd.DataFrame:
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116 """
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117 Read a dataset from a CSV file and return it as a pandas DataFrame.
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118
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119 Args:
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120 data (str): Path to the CSV file containing the dataset.
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121 name (str): Name of the dataset, used in error messages.
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122
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123 Returns:
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124 pandas.DataFrame: DataFrame containing the dataset.
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125
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126 Raises:
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127 pd.errors.EmptyDataError: If the CSV file is empty.
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128 sys.exit: If the CSV file has the wrong format, the execution is aborted.
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129 """
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130 try:
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131 dataset = pd.read_csv(data, sep = '\t', header = 0, index_col=0, engine='python')
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132 except pd.errors.EmptyDataError:
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133 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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134 if len(dataset.columns) < 2:
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135 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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136 return dataset
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137
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138
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139
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140 def OPTGP_sampler(model:cobra.Model, model_name:str, n_samples:int=1000, thinning:int=100, n_batches:int=1, seed:int=0)-> None:
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141 """
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142 Samples from the OPTGP (Optimal Global Perturbation) algorithm and saves the results to CSV files.
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143
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144 Args:
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145 model (cobra.Model): The COBRA model to sample from.
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146 model_name (str): The name of the model, used in naming output files.
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147 n_samples (int, optional): Number of samples per batch. Default is 1000.
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148 thinning (int, optional): Thinning parameter for the sampler. Default is 100.
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149 n_batches (int, optional): Number of batches to run. Default is 1.
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150 seed (int, optional): Random seed for reproducibility. Default is 0.
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151
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152 Returns:
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153 None
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154 """
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155
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156 for i in range(0, n_batches):
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157 optgp = OptGPSampler(model, thinning, seed)
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158 samples = optgp.sample(n_samples)
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159 samples.to_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_OPTGP.csv', index=False)
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160 seed+=1
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161 samplesTotal = pd.DataFrame()
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162 for i in range(0, n_batches):
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163 samples_batch = pd.read_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_OPTGP.csv')
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164 samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
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165
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166 write_to_file(samplesTotal.T, model_name, True)
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167
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168 for i in range(0, n_batches):
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169 os.remove(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_OPTGP.csv')
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170 pass
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171
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172
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173 def CBS_sampler(model:cobra.Model, model_name:str, n_samples:int=1000, n_batches:int=1, seed:int=0)-> None:
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174 """
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175 Samples using the CBS (Constraint-based Sampling) algorithm and saves the results to CSV files.
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176
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177 Args:
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178 model (cobra.Model): The COBRA model to sample from.
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179 model_name (str): The name of the model, used in naming output files.
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180 n_samples (int, optional): Number of samples per batch. Default is 1000.
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181 n_batches (int, optional): Number of batches to run. Default is 1.
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182 seed (int, optional): Random seed for reproducibility. Default is 0.
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183
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184 Returns:
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185 None
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186 """
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187
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188 df_FVA = cobra.flux_analysis.flux_variability_analysis(model,fraction_of_optimum=0).round(6)
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189
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190 df_coefficients = CBS_backend.randomObjectiveFunction(model, n_samples*n_batches, df_FVA, seed=seed)
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191
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192 for i in range(0, n_batches):
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193 samples = pd.DataFrame(columns =[reaction.id for reaction in model.reactions], index = range(n_samples))
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194 try:
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195 CBS_backend.randomObjectiveFunctionSampling(model, n_samples, df_coefficients.iloc[:,i*n_samples:(i+1)*n_samples], samples)
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196 except Exception as e:
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197 utils.logWarning(
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198 "Warning: GLPK solver has failed for " + model_name + ". Trying with COBRA interface. Error:" + str(e),
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199 ARGS.out_log)
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200 CBS_backend.randomObjectiveFunctionSampling_cobrapy(model, n_samples, df_coefficients.iloc[:,i*n_samples:(i+1)*n_samples],
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201 samples)
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202 utils.logWarning(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv', ARGS.out_log)
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203 samples.to_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv', index=False)
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204
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205 samplesTotal = pd.DataFrame()
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206 for i in range(0, n_batches):
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207 samples_batch = pd.read_csv(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv')
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208 samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
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209
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210 write_to_file(samplesTotal.T, model_name, True)
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211
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212 for i in range(0, n_batches):
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213 os.remove(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv')
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214 pass
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215
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216
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217 def model_sampler(model_input_original:cobra.Model, bounds_path:str, cell_name:str)-> List[pd.DataFrame]:
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218 """
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219 Prepares the model with bounds from the dataset and performs sampling and analysis based on the selected algorithm.
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220
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221 Args:
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222 model_input_original (cobra.Model): The original COBRA model.
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223 bounds_path (str): Path to the CSV file containing the bounds dataset.
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224 cell_name (str): Name of the cell, used to generate filenames for output.
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225
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226 Returns:
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227 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
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228 """
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229
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230 model_input = model_input_original.copy()
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231 bounds_df = read_dataset(bounds_path, "bounds dataset")
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232 for rxn_index, row in bounds_df.iterrows():
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233 model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound
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234 model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound
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235
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236
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237 if ARGS.algorithm == 'OPTGP':
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238 OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
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239
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240 elif ARGS.algorithm == 'CBS':
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241 CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed)
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242
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243 df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types)
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244
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245 if("fluxes" not in ARGS.output_types):
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246 os.remove(ARGS.output_path + "/" + cell_name + '.csv')
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247
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248 returnList = []
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249 returnList.append(df_mean)
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250 returnList.append(df_median)
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251 returnList.append(df_quantiles)
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252
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253 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis)
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254
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255 if("pFBA" in ARGS.output_type_analysis):
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256 returnList.append(df_pFBA)
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257 if("FVA" in ARGS.output_type_analysis):
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258 returnList.append(df_FVA)
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259 if("sensitivity" in ARGS.output_type_analysis):
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260 returnList.append(df_sensitivity)
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261
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262 return returnList
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263
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264 def fluxes_statistics(model_name: str, output_types:List)-> List[pd.DataFrame]:
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265 """
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266 Computes statistics (mean, median, quantiles) for the fluxes.
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267
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268 Args:
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269 model_name (str): Name of the model, used in filename for input.
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270 output_types (List[str]): Types of statistics to compute (mean, median, quantiles).
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271
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272 Returns:
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273 List[pd.DataFrame]: List of DataFrames containing mean, median, and quantiles statistics.
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274 """
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275
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276 df_mean = pd.DataFrame()
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277 df_median= pd.DataFrame()
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278 df_quantiles= pd.DataFrame()
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279
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280 df_samples = pd.read_csv(ARGS.output_path + "/" + model_name + '.csv', sep = '\t', index_col = 0).T
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281 df_samples = df_samples.round(8)
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282
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283 for output_type in output_types:
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284 if(output_type == "mean"):
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285 df_mean = df_samples.mean()
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286 df_mean = df_mean.to_frame().T
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287 df_mean = df_mean.reset_index(drop=True)
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288 df_mean.index = [model_name]
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289 elif(output_type == "median"):
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290 df_median = df_samples.median()
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291 df_median = df_median.to_frame().T
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292 df_median = df_median.reset_index(drop=True)
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293 df_median.index = [model_name]
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294 elif(output_type == "quantiles"):
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295 newRow = []
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296 cols = []
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297 for rxn in df_samples.columns:
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298 quantiles = df_samples[rxn].quantile([0.25, 0.50, 0.75])
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299 newRow.append(quantiles[0.25])
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300 cols.append(rxn + "_q1")
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301 newRow.append(quantiles[0.5])
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302 cols.append(rxn + "_q2")
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303 newRow.append(quantiles[0.75])
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304 cols.append(rxn + "_q3")
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305 df_quantiles = pd.DataFrame(columns=cols)
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306 df_quantiles.loc[0] = newRow
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307 df_quantiles = df_quantiles.reset_index(drop=True)
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308 df_quantiles.index = [model_name]
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309
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310 return df_mean, df_median, df_quantiles
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311
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312 def fluxes_analysis(model:cobra.Model, model_name:str, output_types:List)-> List[pd.DataFrame]:
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313 """
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314 Performs flux analysis including pFBA, FVA, and sensitivity analysis.
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315
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316 Args:
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317 model (cobra.Model): The COBRA model to analyze.
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318 model_name (str): Name of the model, used in filenames for output.
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319 output_types (List[str]): Types of analysis to perform (pFBA, FVA, sensitivity).
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320
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321 Returns:
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322 List[pd.DataFrame]: List of DataFrames containing pFBA, FVA, and sensitivity analysis results.
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323 """
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324
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325 df_pFBA = pd.DataFrame()
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326 df_FVA= pd.DataFrame()
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327 df_sensitivity= pd.DataFrame()
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328
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329 for output_type in output_types:
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330 if(output_type == "pFBA"):
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331 model.objective = "Biomass"
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332 solution = cobra.flux_analysis.pfba(model)
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333 fluxes = solution.fluxes
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334 df_pFBA.loc[0,[rxn._id for rxn in model.reactions]] = fluxes.tolist()
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335 df_pFBA = df_pFBA.reset_index(drop=True)
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336 df_pFBA.index = [model_name]
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337 df_pFBA = df_pFBA.astype(float).round(6)
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338 elif(output_type == "FVA"):
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339 fva = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
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340 columns = []
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341 for rxn in fva.index.to_list():
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342 columns.append(rxn + "_min")
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343 columns.append(rxn + "_max")
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344 df_FVA= pd.DataFrame(columns = columns)
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345 for index_rxn, row in fva.iterrows():
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346 df_FVA.loc[0, index_rxn+ "_min"] = fva.loc[index_rxn, "minimum"]
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347 df_FVA.loc[0, index_rxn+ "_max"] = fva.loc[index_rxn, "maximum"]
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348 df_FVA = df_FVA.reset_index(drop=True)
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349 df_FVA.index = [model_name]
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350 df_FVA = df_FVA.astype(float).round(6)
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351 elif(output_type == "sensitivity"):
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352 model.objective = "Biomass"
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353 solution_original = model.optimize().objective_value
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354 reactions = model.reactions
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355 single = cobra.flux_analysis.single_reaction_deletion(model)
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356 newRow = []
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357 df_sensitivity = pd.DataFrame(columns = [rxn.id for rxn in reactions], index = [model_name])
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358 for rxn in reactions:
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359 newRow.append(single.knockout[rxn.id].growth.values[0]/solution_original)
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360 df_sensitivity.loc[model_name] = newRow
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361 df_sensitivity = df_sensitivity.astype(float).round(6)
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362 return df_pFBA, df_FVA, df_sensitivity
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363
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364 ############################# main ###########################################
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365 def main(args :List[str] = None) -> None:
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366 """
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367 Initializes everything and sets the program in motion based on the fronted input arguments.
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368
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369 Returns:
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370 None
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371 """
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372
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373 num_processors = cpu_count()
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374
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375 global ARGS
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376 ARGS = process_args(args)
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377
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378 if not os.path.exists(ARGS.output_path):
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379 os.makedirs(ARGS.output_path)
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380
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381 #model_type :utils.Model = ARGS.model_selector
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382 #if model_type is utils.Model.Custom:
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383 # model = model_type.getCOBRAmodel(customPath = utils.FilePath.fromStrPath(ARGS.model), customExtension = utils.FilePath.fromStrPath(ARGS.model_name).ext)
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384 #else:
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385 # model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)
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386
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387 model = utils.build_cobra_model_from_csv(ARGS.model_upload)
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388
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389 validation = utils.validate_model(model)
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390
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391 print("\n=== VALIDAZIONE MODELLO ===")
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392 for key, value in validation.items():
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393 print(f"{key}: {value}")
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394
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395 #Set solver verbosity to 1 to see warning and error messages only.
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396 model.solver.configuration.verbosity = 1
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397
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398 ARGS.bounds = ARGS.input.split(",")
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399 ARGS.bounds_name = ARGS.names.split(",")
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400 ARGS.output_types = ARGS.output_type.split(",")
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401 ARGS.output_type_analysis = ARGS.output_type_analysis.split(",")
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402
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403
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404 results = Parallel(n_jobs=num_processors)(delayed(model_sampler)(model, bounds_path, cell_name) for bounds_path, cell_name in zip(ARGS.bounds, ARGS.bounds_name))
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405
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406 all_mean = pd.concat([result[0] for result in results], ignore_index=False)
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407 all_median = pd.concat([result[1] for result in results], ignore_index=False)
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408 all_quantiles = pd.concat([result[2] for result in results], ignore_index=False)
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409
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410 if("mean" in ARGS.output_types):
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411 all_mean = all_mean.fillna(0.0)
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412 all_mean = all_mean.sort_index()
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413 write_to_file(all_mean.T, "mean", True)
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414
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415 if("median" in ARGS.output_types):
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416 all_median = all_median.fillna(0.0)
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417 all_median = all_median.sort_index()
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418 write_to_file(all_median.T, "median", True)
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419
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420 if("quantiles" in ARGS.output_types):
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421 all_quantiles = all_quantiles.fillna(0.0)
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422 all_quantiles = all_quantiles.sort_index()
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423 write_to_file(all_quantiles.T, "quantiles", True)
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424
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425 index_result = 3
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426 if("pFBA" in ARGS.output_type_analysis):
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427 all_pFBA = pd.concat([result[index_result] for result in results], ignore_index=False)
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428 all_pFBA = all_pFBA.sort_index()
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429 write_to_file(all_pFBA.T, "pFBA", True)
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430 index_result+=1
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431 if("FVA" in ARGS.output_type_analysis):
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432 all_FVA= pd.concat([result[index_result] for result in results], ignore_index=False)
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433 all_FVA = all_FVA.sort_index()
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434 write_to_file(all_FVA.T, "FVA", True)
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435 index_result+=1
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436 if("sensitivity" in ARGS.output_type_analysis):
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437 all_sensitivity = pd.concat([result[index_result] for result in results], ignore_index=False)
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438 all_sensitivity = all_sensitivity.sort_index()
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439 write_to_file(all_sensitivity.T, "sensitivity", True)
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440
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441 pass
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442
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443 ##############################################################################
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444 if __name__ == "__main__":
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445 main() |
